Smoking Needs Assessment Analysis

Trend over Time

There are two primary sources of smoking prevalence data: Quality and Outcomes Framework (QOF) extracted from GP data systems, and the Annual Population Survey (APS).

According to FingerTips regarding QOF: “The percentage of patients who do not have their smoking status recorded varies from 40% to less than 1%, but the characteristics of these patients are not known. Recording of smoking status has been shown to vary between groups; women, older people and those with chronic conditions were more likely to have their smoking status recorded.” However, a study by Honeyford K, et al “found that the proportion of patients recorded as smokers… is a good estimate of the actual smoking prevalence of the practice.”

The APS prevalence, however, is based on a sample of the population of Birmingham. In 2023, 840 people participated in the survey. This is 0.1% of the 857,455 people aged 18+ in the 2021 census. As can be seen in the figure below, this small denominator means there is large uncertainty on the prevalence value. As a result, the increase in prevalence seen in 2023 is not statistically significant.

Smoking Cessation Services

The map below shows the smoking QOF prevalence (2023/24) and the location of BCC commissioned smoking cessation services.

GP Patient Demographics

For data security, all counts relating to GP registrations are rounded to the nearest 5.

GP_eths_ojs = aq.from(transpose(GP_eths))
GP_Age_Gender_ojs = aq.from(transpose(GP_Age_Gender))
GP_IMD_ojs = aq.from(transpose(GP_IMD))

// Extract unique Practice Names from the table
Practice_Names = GP_eths_ojs.array("GP_Name").filter((v, i, a) => a.indexOf(v) === i).sort()

/* Get input from user */
viewof variable = Inputs.select(Practice_Names, {label: "Choose a Practice"})

/*  Filter based on selected variable */
GP_eths_filtered = GP_eths_ojs.params({ variable }).filter((d) => d.GP_Name === variable)
GP_Age_Gender_filtered = GP_Age_Gender_ojs.params({ variable }).filter((d) => d.GP_Name === variable)
GP_IMD_filtered = GP_IMD_ojs.params({ variable }).filter((d) => d.GP_Name === variable)

Plot.plot({
  width: 800, 
  height: 300, 
  marginLeft: 80,
  marginBottom: 120,
  /* Set axis labels */
  x: { 
      label: "Ethnicity",
      tickRotate: -45
      },
  y: { label: "Number of patients" },
  marks: [
    Plot.barY(GP_eths_filtered, {
      y: "N",
      x: "Ethnicity",
      fill: "#7570b3"
    })
  ]
})

Plot.plot({
  width: 320, 
  height: 240, 
  marginLeft: 80,
  marginTop: 60,
  /* Set axis labels */
  x: { 
    label: "IMD Quintile",
    type: "band", // explicitly treat x as categorical
    domain: ["1", "2", "3", "4", "5", "Unknown"]
    },
  y: { label: "Number of patients" },
  marks: [
    Plot.barY(GP_IMD_filtered, {
      y: "N",
      x: "IMD_Quintile",
      fill: "#7570b3"
    })
  ]
})

Plot.plot({
  width: 480, 
  height: 200, 
  marginLeft: 40,
  x: { 
    label: "Age Group",
    // type: "band", 
    domain: ["0-15", "16-24", "25-34", "35-44", "45-54", "55-64", "65-74", "75+" ] 
  },
  y: { label: "Number of patients" },
  color: {
    legend: true,
    scheme: "Dark2"
  },
  marks: [
    Plot.barY(GP_Age_Gender_filtered, {
      x: "Age_group",
      y: "N",
      fill: "Gender"
      }),
  ]
})

Pharmacy/Business Customer Demographics

The demographics of pharmacy customers are estimated based on those living within 1km of the pharmacy using the 2021 ONS census. Only Birmingham residents are included in the analysis.

pharm_eths_ojs = aq.from(transpose(pharm_eths))
pharm_Age_Gender_ojs = aq.from(transpose(pharm_Age_Gender))
pharm_IMD_ojs = aq.from(transpose(pharm_IMD))

// Extract unique Practice Names from the table
Pharmacy_Names = pharm_eths_ojs.array("Name_Code").filter((v, i, a) => a.indexOf(v) === i).sort()

/* Get input from user */
viewof variable2 = Inputs.select(Pharmacy_Names, {label: "Choose a Pharmacy"})

/*  Filter based on selected variable */
pharm_eths_filtered = pharm_eths_ojs.params({ variable2 }).filter((d) => d.Name_Code === variable2)
pharm_Age_Gender_filtered = pharm_Age_Gender_ojs.params({ variable2 }).filter((d) => d.Name_Code === variable2)
pharm_IMD_filtered = pharm_IMD_ojs.params({ variable2 }).filter((d) => d.Name_Code === variable2)

Plot.plot({
  width: 800,
  height: 300,
  marginLeft: 80,
  marginBottom: 120,
  /* Set axis labels */
  x: {
      label: "Ethnicity",
      tickRotate: -45
      },
  y: { label: "Number of Residents" },
  marks: [
    Plot.barY(pharm_eths_filtered, {
      y: "Est_Num",
      x: "Ethnic_Group",
      fill: "#7570b3"
    })
  ]
})

Plot.plot({
  width: 320,
  height: 240,
  marginLeft: 80,
  marginTop: 60,
  /* Set axis labels */
  x: {
    label: "IMD Quintile",
    type: "band", // explicitly treat x as categorical
    domain: ["1", "2", "3", "4", "5"]
    },
  y: { label: "Number of Residents" },
  marks: [
    Plot.barY(pharm_IMD_filtered, {
      y: "Est_Num",
      x: "IMD_quintile",
      fill: "#7570b3"
    })
  ]
})

Plot.plot({
  width: 480,
  height: 200,
  marginLeft: 40,
  x: {
    label: "Age Group",
    // type: "band",
    domain: ["0-15", "16-24", "25-34", "35-44", "45-54", "55-64", "65-74", "75+" ]
  },
  y: { label: "Number of Residents" },
  color: {
    legend: true,
    scheme: "Dark2"
  },
  marks: [
    Plot.barY(pharm_Age_Gender_filtered, {
      x: "Age_group",
      y: "Est_Num",
      fill: "Sex"
      }),
  ]
})

Referrals from Hospital